基于深度学习的识别和成熟度评估,在形光束计算机断层扫描图像中,对腰椎部部 suture进行了评估
Zehua Jin1,2, Yuhua Shan2,3, Jianxing Feng4
1Department of Stomatology, Shanghai East Hospital, Shanghai, China.
Progress in orthodontics
|March 2, 2026
概括
这项研究引入了一种深度学习系统,用于评估从形束计算机断层扫描进行的肌肉尾 suture 成熟. 这种人工智能工具有助于确定尾延伸治疗的最佳时间,改善治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 矯正牙科 矯正牙科是一種矯正牙科.
背景情况:
- 大缺陷需要精确的时间来进行有效的延伸疗法.
- 对治疗规划来说,评估牙上的成熟度至关重要.
- 目前用于评估的手工方法是主观的,耗时的.
研究的目的:
- 开发一种深度学习系统,用于自动识别腹肌和成熟阶段.
- 为指导大延伸治疗决策提供定量和可重复性分析.
- 克服临床实践中主观手动分期的局限性.
主要方法:
- 使用了505个圆束计算机断层扫描 (CBCT) 扫描的数据集.
- 采用了包括3D U-Net和3D ResNet50在内的双网络架构,用于线识别和成熟分类.
- 模型性能与专家评估相对验证,使用准确性,F1得分和AUC等指标.
主要成果:
- 该系统在分类结成熟阶段方面取得了很高的准确性 (例如,两阶段分类的0.922).
- 3D U-Net组件准确地定位了中心,平均欧几里德距离为2.31毫米.
- 深度学习模型表现出高效的评估,在约46秒内完成任务.
结论:
- 开发的深度学习系统从CBCT扫描中准确地定位和评估腰椎部结成熟度.
- 这种人工智能驱动的方法通过减少评估时间和观察者之间的变化来增强尾延伸治疗的临床决策.
- 该系统为优化干预时间,防止治疗不足或过度治疗提供了可靠的参考.
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